class_docstr: The performance class encapsulates the metrics that are recorded for
  configurations.
docstr: Initializes a new performance object from the given 1D dictionary.
  
  Metrics are expected to be provided via `<metric>_mean` and
  `<metric>_std` keys.
docstr_tok: ["Initializes","a","new","performance","object","from","the","given","1D","dictionary",".","Metrics","are","expected","to","be","provided","via","`","<","metric",">","_mean","`","and","`","<","metric",">","_std","`","keys","."]
code: def from_dict(cls, metrics: dict[str, float | int]) -> Performance:
      """
      Initializes a new performance object from the given 1D dictionary.
  
      Metrics are expected to be provided via `<metric>_mean` and
      `<metric>_std` keys.
      """
      kwargs = {
          m: Metric(metrics[f"{m}_mean"], metrics[f"{m}_std"])
          for m in cls.metrics()
      }
      return Performance(**kwargs)
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/evaluations/metrics/performance.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import cast, Dict, List, Union
  import numpy
  import pandas
  from .metric import Metric
label_desc: Use image node_id 1 for calling the Performance obj's underlying member method code with example usage: obj.from_dict(cls, metrics) and returns: Performance